Impact of missing pedigrees in single-step genomic evaluation

Abstract

Context A common problem in mixed model-based genetic evaluation schemes for livestock is that cohorts of animals differ systematically in mean genetic merit, for example, due to missing pedigree. This can be modelled by fitting genetic groups. Single-step genomic evaluation (ssGBLUP) combining information from genotyped and non-genotyped individuals has become routine, but little is known of the effects of unknown parents in this context. Aims To investigate the effects of missing pedigrees on accuracy and bias of predicted breeding values for ssGBLUP analyses. Methods A simulation study was used to examine alternative ways to account for genetic groups in ssGBLUP, for multi-generation data with strong selection and rapidly increasing numbers of genotyped animals in the most recent generations. Key results Results demonstrated that missing pedigrees can markedly impair predicted breeding values. With selection, alignment of genomic and pedigree relationship matrices is essential when fitting unknown parent groups (UPG). Genomic relationships are complete; that is, they ‘automatically’ reference the genomic base, which typically differs from the genetic base for pedigreed animals. This can lead to biased comparisons between genotyped and non-genotyped animals with unknown parents when the two categories of animals are assigned to the same UPG. Allocating genotyped individuals to a separate UPG across all generations for each strain or breed was shown to be a simple and effective way to reduce misalignment bias. In contrast, fitting metafounders modified pedigree-based relationships to account for ancestral genomic relationships and inbreeding rather than the genomic relationship matrix. Thus, no bias due to different types of animals assigned to the same metafounders was apparent. Overall, fitting metafounders yielded slightly higher correlations between true and predicted breeding values than did UPG models, which assume genetic groups to be unrelated. Conclusions Missing pedigrees are more problematic with ssGBLUP than for analyses considering pedigree-based relationships only. UPG models with separation of genotyped and non-genotyped individuals and analyses fitting metafounders yielded comparable predictions of breeding values in terms of accuracy and bias. Implications A previously unidentified incompatibility between alignment of founder populations and assignment of genotyped and non-genotyped animals to the same UPG has been reported. Implementation of the proposed strategy to reduce ‘double counting’ is straightforward and can improve results of ssGBLUP analyses.